[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119530-en":3,"doc-seo-119530-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119530,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Big data, machine learning, and digital twin assisted additive manufacturing - A review","Additive manufacturing (AM) has generated vast datasets that can be leveraged for process optimization, supply-chain improvement, and real-time monitoring. This review surveys how machine learning and digital twin–assisted approaches are used within AM, covering research directions and methods such as material analysis, design and process parameter optimization, defect detection and monitoring, and sustainability evaluation. It further analyzes the evolution from current research status to technical implementation, arguing that big data, machine learning, and digital twins are intrinsically connected. The paper proposes an integrated framework to enhance AM efficiency, accuracy, and sustainability.","Materials & Design 244 (2024) 113086  \nContents lists available at ScienceDirect  \nMaterials & Design  \njournal [homepage: www.elsevier.com/locate/matdes](homepage: www.elsevier.com/locate/matdes)  \n| Big data, machine learning, and digital twin assisted additive manufacturing: A review\u003Cbr>Liuchao Jin a,b,c, Xiaoya Zhai d,a, Kang Wang e,f , Kang Zhang a,g, Dazhong Wu h, Aamer Naziri,j, Jingchao Jiang k ,∗ , Wei-Hsin Liao a,l,∗\u003Cbr>a Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, China\u003Cbr>b Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, 518055, China\u003Cbr>c Shenzhen Key Laboratory of Soft Mechanics & Smart Manufacturing, Southern University of Science and Technology, Shenzhen, 518055, China d School of Mathematical Sciences, University of Science and Technology of China, Hefei, 230026, China\u003Cbr>e School of Nursing, The Hong Kong Polytechnical University, Hong Kong, China\u003Cbr>f School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China\u003Cbr>g Nano and Advanced Materials Institute Limited, Hong Kong Science Park, Hong Kong, China\u003Cbr>h Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, 32816, FL, USA\u003Cbr>i Department of Mechanical Engineering, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia\u003Cbr>j Interdisciplinary Research Center for Advanced Materials, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia k Department of Engineering, University of Exeter, Exeter, United Kingdom\u003Cbr>l Institute of Intelligent Design and Manufacturing, The Chinese University of Hong Kong, Hong Kong, China |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Additive manufacturing Big data\u003Cbr>Machine learning Digital twin\u003Cbr>Data-driven |  | Additive manufacturing (AM) has undergone significant development over the past decades, resulting in vast amounts of data that carry valuable information. Numerous research studies have been conducted to extract insights from AM data and utilize it for optimizing various aspects such as the manufacturing process, supply chain, and real-time monitoring. Data integration into proposed digital twin frameworks and the application of machine learning techniques is expected to play pivotal roles in advancing AM in the future. In this paper, we provide an overview of machine learning and digital twin-assisted AM. On one hand, we discuss the research domain and highlight the machine-learning methods utilized in this field, including material analysis, design optimization, process parameter optimization, defect detection and monitoring, and sustainability. On the other hand, we examine the status of digital twin-assisted AM from the current research status to the technical approach and offer insights into future developments and perspectives in this area. This review paper aims to examine present research and development in the convergence of big data, machine learning, and digital twin-assisted AM. Although there are numerous review papers on machine learning for additive manufacturing and others on digital twins for AM, no existing paper has considered how these concepts are intrinsically connected and interrelated. Our paper is the first to integrate the three concepts big data, machine learning, and digital twins and propose a cohesive framework for how they can work together to improve the efficiency, accuracy, and sustainability of AM processes. By exploring latest advancements and applications within these domains, our objective is to emphasize the potential advantages and future possibilities associated with integration of these technologies in AM. |\n\n1. Introduction  \nAdditive manufacturing (AM), also known as 3D printing, has revolutionized the manufacturing industry by offering unprecedented design freedom [1–5], reducing material waste [6–11], and enabling the  \nproduct","cbCaimRhVTLuYvvg","https://ap.wps.com/l/cbCaimRhVTLuYvvg","pdf",14657431,1,53,"English","en",105,"# Introduction\n## Additive manufacturing progress and data challenges\n## Big data analytics for AM insights\n# Machine learning in additive manufacturing\n## Material analysis and characterization\n## Design and process parameter optimization\n## Defect detection and monitoring\n## Sustainability and data-driven evaluation\n# Digital twin-assisted additive manufacturing\n## Research status and technical approaches\n## Future directions and perspectives","[{\"question\":\"What is the main purpose of this review paper?\",\"answer\":\"To examine current research and development at the intersection of big data, machine learning, and digital twin–assisted additive manufacturing, and to present how these concepts work together to improve AM performance.\"},{\"question\":\"Which machine-learning topics are discussed for additive manufacturing?\",\"answer\":\"Material analysis, design optimization, process parameter optimization, defect detection and monitoring, and sustainability-related applications are highlighted.\"},{\"question\":\"How does the paper describe the role of digital twins in additive manufacturing?\",\"answer\":\"It reviews digital twin–assisted AM from current research status to technical approaches, and provides insights into future developments in the field.\"}]","Big data, machine learning, and digital twin assisted additive manufacturing - 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